World Journal of Peri & Neonatology Vol. 5, No. 2, Fall 2022
Genetic Association between ITPKC rs28493229 Polymorphism and Susceptibility to Kawasaki Disease: A Meta-Analysis
Seyed Alireza Dastgheib1, Fatemeh Asadian2, Azadeh Tahooni3, Reza Bahrami4*, Mahmood Noorishadkam5,6, Seyed Reza Mirjalili5,6, Hossein Neamatzadeh6,7
1 Department of Medical Genetics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Medical Laboratory Sciences, School of Paramedical Science, Shiraz University of Medical Sciences, Shiraz, Iran
3 Department of Cardiology, Firoozgar Hospital Research Center, Iran University of Medical Sciences, Tehran, Iran
4 Neonatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
5 Department of Pediatrics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
6 Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
7 Department of Medical Genetics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Received: 26 September 2022 Revised: 05 November 2022 Accepted: 08 December 2022
ARTICLE INFO ABSTRACT
Corresponding author:
Reza Bahrami Email:
Background: Studies investigating the association between ITPKC rs28493229 polymorphisms and Kawasaki disease (KD) risk found inconsistent data. Thus, we performed this meta-analysis to combine and analyze the available studies to get a precise estimation of the association.
Methods: Relevant studies identified in the PubMed, Web of Science, Scopus, and CNKI databases were used to perform a meta-analysis. Pooled odds ratios (OR) with a 95% confidence interval (95% CI) were calculated under fixed- and random-effects models to appraise the association.
Results: A total of eight case-control studies with 2,721 KD cases and 5,307 controls were selected. The results showed a statistically significant association between ITPKC rs28493229 polymorphism and an increased risk of KD under all five genetic models, i.e., allele (C vs. G: OR = 1.434, 95% CI 1.209-1.700, P ≤ 0.001), homozygote (CC vs. GG: OR = 2.085, 95% CI 1.423-3.055, P ≤ 0.001), heterozygote (CG vs. GG: OR = 1.530, 95% CI 1.359-1.722, P ≤ 0.001), dominant (CC+CG vs. GG: OR = 1.490, 95% CI 1.229-1.806, P ≤ 0.001), and recessive (CC vs. CG + GG: OR = 1.799, 95% CI 1.231-2.629, P = 0.002) in the overall population. When stratified by country, there was a significant association among Taiwanese.
Conclusion: Our meta-analysis results supported that the ITPKC rs28493229 polymorphism is strongly associated with susceptibility to KD.
Keywords:
Kawasaki Disease;
ITPKC Gene;
Vasculitis;
Polymorphism;
Association
[
Review Article
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ITPKC rs28493229 and Kawasaki Disease
84 World J Peri & Neonatol 2022; Vol. 5; No. 2
Introduction
awasaki disease (KD; OMIM 300530) is an acute vasculitis of the medium- and small-sized arteries of childhood.1-3 Population-based age adjusted- data showed that children under five years of age are mostly affected by KD with a male predominance. In the last decades, KD has been the Primary cause of acquired heart disease in children in East Asia, Europe, and North America.1,4 It is noticeably more prevalent in Japan and other Asian countries (69-240 per 100.000) compared with Western countries (4-15 per 100.000) in children under the age of 5 years.5-7 As the available data for specific diagnostic tests of KD are limited, the diagnosis is based on the presence of clinical criteria.8,9 In 2004, American Heart Association (AHA) mounted diagnostic guidelines for the initial estimate, treatment in the acute phase, and long-term management of KD.10 Standard treatment in acute KD consists of a single dose of high-dose intravenous immunoglobulin (IVIG) at 2 g/kg, preferably given within 10 days after the onset of fever.11-13
Although recent studies provide new insights into the mechanisms of immune activation in KD14, the exact etiology of KD is still unknown. It is supposed that KD reflects an abnormal inflammatory response to one or more infectious agents or toxins in genetically predisposed people.15 The prevalence of KD is increased in siblings with a history of KD or children with parental history of KD.16 Inositol 1,4,5-trisphosphate (IP3) is a second messenger which transduces signals from cell surface receptors in T cells.
Inositol 1,4,5-trisphosphate 3-kinase (ITPK) phosphorylates IP3 and serves as a negative regulator of the Ca2+ /nuclear factor of the activated T-cell signaling pathway.17,18 Genome-wide association studies (GWAS) have emphasized the importance of functional variants in the ITPKC gene, which is a negative regulator of T-cell activation through the Ca2+/NFAT signaling pathway (Figure 1).
Functional polymorphisms in the ITPKC gene may contribute to increased T cell activation, increased expression of cytokines, and immune hyper-reactivity in KD.14,19,20
Figure 1. The ITPKC signaling pathways contribution to the susceptibility or clinical status of Kawasaki disease20
K
The C allele of the responsible SNP (rs28493229, C allele) alters the splicing efficiency of ITPKC intron 1 and then reduces the amount of mature ITPKC mRNA, which in turn results in increased signaling through the calcineurin/NFAT pathway and cell activation, and may contribute to immune hyperactivity in KD.19
The ITPKC gene, also recognized as MDR1, is located on chromosome 19q13.2.
The ITPKC gene product is widely localized in the nucleus and cytoplasm and has both nuclear import and nuclear export activity. In 2008, Onouchi et al. provided new insights into the mechanisms of immune activation in Kawasaki disease and emphasizes the role of ITPKC rs28493229 polymorphism in the pathogenesis of KD.14 However, some studies have shown no association between KD and polymorphism rs28493229 in ITPKC. For example, in 2018, Kim et al. reported that the ITPKC rs28493229 polymorphism has a protective effect against KD symptoms.21 Thus, the association of the ITPKC rs28493229 polymorphism with KD risk is still ambiguous. Meta-analysis offers an opportunity to aggregate information from multiple studies, improving statistical power by increasing the sample size to exactly evaluate genetic polymorphisms outcomes on disease susceptibility. To assess the association between ITPKC rs28493229 polymorphism and the risk of KD, we performed a meta-analysis based on all eligible case-control studies published up to March 05, 2020.
Materials and Methods
Literate Search Strategy: Ethical approval was not required for this study, as it is a systematic review and meta-analysis. This work was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We have performed a comprehensive literature search on electronic databases including PubMed, EMBASE, Wed of Science, Elsevier, Google Scholar,
Cochrane Library, SciELO, SID, WanFang, VIP, Chinese Biomedical Database (CBD), and Chinese National Knowledge Infrastructure (CNKI) to identify all relevant studies on the association between ITPKC rs28493229 polymorphism and Kawasaki disease risk up to March 05, 2020.
Combinations of the following MeSH terms and keywords were used in the search:
(“Kawasaki Disease” OR “KD” OR
“Mucocutaneous Lymph Node Syndrome”) AND (‘’Inositol 1,4,5-trisphosphate 3-kinase C’’ OR ‘’ ITPKC’’ OR ‘’IP3KC’’ OR ‘’IP3- 3KC’’) AND (‘’Gene’’ OR ‘’Single- Nucleotide Polymorphism” or “SNP” OR
‘’Polymorphism’’ OR ‘’Genotype’ OR
‘’Allele’’ OR ‘’Variation’’ OR ‘’Mutation’’).
The search was limited to human studies published in English, Farsi, and Chinese language. We also reviewed the references list of relevant reviews and eligible publications to find other potential sources.
Inclusion Criteria: Studies meeting the following criteria were included: 1) studies with case-control or cohort design; 2) studies evaluated the association of ITPKC rs28493229 polymorphism and Kawasaki disease risk; 3) studies with available and sufficient data for calculating an odds ratio (OR) with 95% confidence interval (CI). The following exclusion criteria were also used:
1) animal studies or in vitro studies; 2) studies with sufficient data on genotype frequencies;
3) studies evaluated the association of other polymorphism of ITPKC; 4) linkage studies or family-based studies (including sibling, twins and trios-parents studies); 5) abstracts, case reports, commentaries, editorials, conference articles, reviews, proceedings and meta- analyses; and 6) duplicates or overlapping studies. If more than one study was published by the same author(s) using repeated or overlapped data, the studies with the largest sample size or the most recently published study were included in the meta-analysis.
Data Extraction: Two authors independently reviewed all eligible articles and extracted all necessary data according to
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the inclusion criteria. For any discrepancies, a discussion was made to reach an agreement.
If the two authors could not reach a consensus, then a third investigator was consulted to resolve the dispute and a final decision was made by the majority of the votes. For each eligible study, the following data were collected: first author name, year of publication, country of origin, ethnicity (Caucasian, Asian, African, Mixed populations), source of controls (hospital- based or population-based), genotyping methods, sample size, allele and genotype frequency of ITPKC rs28493229 polymorphism in cases and controls, Minor Allele Frequency (MAFs) and Hardy- Weinberg equilibrium (HWE) in healthy controls. In this meta-analysis different case- control groups or cohorts in one publication were considered independent studies.
Statistical Analysis: The strength of the association between ITPKC rs28493229 polymorphism and KD risk was estimated by odds ratio (OR) with the corresponding 95%
confidence intervals (CIs). The significance of pooled ORs was tested by Z-test, in which P < 0.05 was considered significant. The association of ITPKC rs28493229 polymorphism was estimated under five genetic models, i.e., allele (C vs. G), homozygote (CC vs. GG), heterozygote (CG vs. GG), dominant (CC + CG vs. GG), and recessive (CC vs. CG + GG). The chi-square test was used to evaluate the between-study heterogeneity. If P < 0.10, it was considered to have significant heterogeneity in statistics.
Moreover, I2 test to quantify the heterogeneity, which ranges from 0 to 100% and represents the proportion of between-study variability attributable to heterogeneity rather than chance (I2 < 25%, no heterogeneity; I2 25-50%, moderate heterogeneity; I2 > 50%, large or extreme heterogeneity).22 When significant heterogeneity existed, we selected a random- effects model (the DerSimonian and Laird method) for statistics. Otherwise, the fixed- effects model (the Mantel-Haenszel method) was used. The Hardy-Weinberg equilibrium
(HWE) of the controls was evaluated by Fisher exact test and a p-value less than 0.05 was considered significant disequilibrium (HWE- violating). Subgroup analyses by ethnicity, country, source of controls, and genotyping methods were performed to explore the potential sources of between-study heterogeneity in the meta-analysis. One-way sensitivity analysis, by which a single study in the meta-analysis was omitted each time to reflect the influence of the individual data set for the pooled OR, was carried out to assess the stability of the results. Moreover, sensitivity analysis was done by excluding HWE-violating studies. Begg’s funnel plots were generated to assess the potential influences of the publication bias on the results. An asymmetrical plot usually indicates the existence of publication bias.23 Moreover, Egger’s linear regression test which measures funnel plot asymmetry using a natural logarithm scale of OR was performed to evaluate the symmetry of the plot. All statistical tests were performed using CMA software. All P values in the meta-analysis were 2-sided, and P values less than 0.05 were considered significant.
Results
Characteristics of Selected Studies: The process of the literature search and selection is shown in figure 2. Initially, our search strategy yielded 101 possibly relevant articles.
Twenty-nine studies were removed due to duplication, and 79 articles were removed because they were not case-control studies, human research, or without available data and previous meta-analyses. Finally, a total of eight case-control studies with 2,721 KD cases and 5,307 controls were included in the meta-analysis.14,17,18,21,24-27 The characteristics of the included studies are summarized in table 1. KD cases in the studies ranged from 17 to 637 and selected studies were published between January 2008 and January 2018. In terms of ethnicity, seven studies were performed among Asians and one study was conducted among Caucasians.
Figure 2. Flowchart of literature search and selection process
The studies have been carried out in Japan, Taiwan, China, South Korea, and Australia.
As seen in table 1, three genotyping methods including TaqMan, sequencing, and RFLP- PCR were used to genotype the ITPKC rs28493229 polymorphism. The allele, genotype, and minor allele frequency (MAF) distributions for ITPKC rs28493229 polymorphism in KD cases and healthy controls were presented in table 1. Hardy-Weinberg equilibrium (HWE) was calculated for all eight publications and P < 0.05 was considered as a departure from HWE (Table 1).
Quantitative Data Synthesis: The summary results for the association between
ITPKC rs28493229 polymorphism and KD risk are shown in table 2. Overall, pooled data revealed a significant association between the ITPKC rs28493229 polymorphism and an increased risk of KD under all five genetic models, i.e., allele (C vs. G: OR = 1.434, 95%
CI 1.209-1.700, P ≤ 0.001), homozygote (CC vs. GG: OR = 2.085, 95% CI 1.423-3.055, P ≤ 0.001, Figure 2B), heterozygote (CG vs.
GG: OR = 1.530, 95% CI 1.359-1.722, P ≤ 0.001), dominant (CC+CG vs. GG:
OR = 1.490, 95% CI 1.229-1.80 been carried out6, P ≤ 0.001), and recessive (CC vs.
CG+GG: OR = 1.799, 95% CI 1.231-2.629, P = 0.002) (Figure 3A-E).
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Table 1. Characteristics of Studies Included in This Meta-Analysis Author/Year Country (Ethnicity) SOC Genotyping
Methods
Case/Control Cases Controls MAFs HWE
Genotypes Allele Genotypes Allele
GG GC CC G C GG GC CC G C
Onouchi 2008 Asians PB TaqMan 637/1034 376 234 27 986 288 756 249 29 1761 307 0.148 0.126 Onouchi 2011 Japan(Asian) PB Sequencing 546/938 330 191 25 851 241 662 261 15 1585 291 0.155 0.059 Chi 2011 Taiwan(Asian) NS TaqMan 385/1158 323 61 1 707 63 1008 147 3 2163 153 0.066 0.327 Lin 2011 Taiwan(Asian) NS Sequencing 280/492 236 43 1 515 45 454 37 1 945 39 0.040 0.787 Kuo 2011 Taiwan(Asian) PB TaqMan 334/1131 282 50 2 614 54 981 142 8 2104 158 0.070 0.256 Peng 2012 China(Asian) HB PCR-RFLP 223/318 195 27 1 417 29 274 40 4 588 48 0.075 0.078 Natividad 2013 Australia(Caucasian) HB Sequencing 17/26 16 1 0 33 1 22 4 0 48 4 0.077 0.670
Kim 2018 Korea(Asian) HB TaqMan 299/210 231 64 4 526 72 178 32 0 388 32 0.076 0.232
SOC: source of control; PB: population-based; HB: hospital-based; NS: not stated; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; MAF: minor allele frequency; HWE: Hardy-Weinberg equilibrium
Table 2. Summary Risk Estimates for Association of ITPKC rs28493229 Polymorphism with Kawasaki Disease Risk Subgroup Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
Overall C vs. G Random 51.87 0.042 1.434 1.209-1.700 4.143 ≤0.001 0.386 0.216 CC vs. GG Fixed 13.11 0.330 2.085 1.423-3.055 3.768 ≤0.001 0.763 0.408 CG vs. GG Fixed 49.08 0.056 1.530 1.359-1.722 7.029 ≤0.001 0.173 0.225 CC+CG vs. GG Random 54.33 0.032 1.490 1.229-1.806 4.061 ≤0.001 0.173 0.193 CC vs. CG+GG Fixed 8.69 0.362 1.799 1.231-2.629 3.033 0.002 0.548 0.523 Taiwanese C vs. G Random 59.37 0.085 1.360 1.116-1.658 3.043 0.002 0.548 0.387 CC vs. GG Fixed 0.00 0.887 1.048 0.327-3.358 0.079 0.937 0.296 0.260 CG vs. GG Random 56.84 0.099 1.471 1.058-2.045 2.298 0.022 1.000 0.197 CC+CG vs. GG Random 58.89 0.088 1.407 1.142-1.734 3.203 0.001 1.000 0.191 CC vs. CG+GG Fixed 0.00 0.903 1.006 0.314-3.221 0.010 0.992 0.296 0.255 Source of Controls
PB C vs. G Random 61.46 0.075 1.103 0.591-2.060 0.308 0.758 1.000 0.642
CC vs. GG Random 60.77 0.110 1.326 0.073-5.977 0.190 0.849 NA NA CG vs. GG Fixed 34.06 0.219 1.206 0.855-1.701 1.067 0.286 1.000 0.506 CC+CG vs. GG Random 51.97 0.125 1.133 0.634-2.024 0.422 0.673 1.000 0.587 CC vs. CG+GG Random 58.45 0.121 1.005 0.173-5.829 0.006 0.995 NA NA
HB C vs. G Fixed 45.00 0.162 1.541 1.366-1.739 7.024 ≤0.001 0.296 0.142
CC vs. GG Fixed 39.16 0.193 2.214 1.482-3.308 3.879 ≤0.001 1.000 0.671 CG vs. GG Random 60.38 0.080 1.544 1.218-1.956 3.594 ≤0.001 0.296 0.421 CC+CG vs. GG Random 59.82 0.083 1.578 1.256-1.982 3.913 ≤0.001 0.296 0.280 CC vs. CG+GG Fixed 41.70 0.180 1.888 1.267-2.812 3.126 0.002 1.000 0.759
Table 2. Summary Risk Estimates for Association of ITPKC rs28493229 Polymorphism with Kawasaki Disease Risk (Continue) Subgroup Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
Genotyping Methods
TaqMan C vs. G Fixed 42.31 0.158 1.493 1.308-1.705 5.928 ≤0.001 0.734 0.441 CC vs. GG Fixed 0.00 0.596 1.751 1.073-2.857 2.243 0.025 0.734 0.897 CG vs. GG Random 50.90 0.106 1.500 1.190-1.891 3.435 0.001 0.734 0.295 CC+CG vs. GG Random 55.38 0.081 1.505 1.185-1.912 3.354 0.001 0.734 0.357 CC vs. CG+GG Fixed 0.00 0.661 1.476 0.908-2.400 1.571 0.116 0.734 0.942 Sequencing C vs. G Fixed 40.66 0.185 1.606 1.349-1.911 5.337 ≤0.001 1.000 0.833 CC vs. GG Fixed 0.00 0.704 3.248 1.719-6.136 3.629 ≤0.001 NA NA CG vs. GG Random 52.74 0.120 1.641 1.053-2.557 2.189 0.029 1.000 0.308 CC+CG vs. GG Fixed 44.85 0.163 1.656 1.357-2.021 4.967 ≤0.001 1.000 0.807 CC vs. CG+GG Fixed 0.00 0.722 2.874 1.528-5.408 3.274 0.001 NA NA PB: population-based; HB: hospital-based; NA: Not Applicable
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90 World J Peri & Neonatol 2022; Vol. 5; No. 2
Figure 3. Forest plot for association of ITPKC rs28493229 polymorphism with risk of KD in the overall population. A: allele model (C vs. G); B: homozygote model (CC vs. GG); C: heterozygote model (CG vs. GG);
D: dominant model (CC+CG vs. GG); and E: recessive model (CC vs. CG+GG)
Moreover, we performed subgroup analysis based on country of origin and ethnicity. Because of insufficient data, we did not analyze the association between ITPKC rs28493229 polymorphism and KD risk in Caucasians. We analyzed the association between ITPKC rs28493229 polymorphism and KD risk in Asians, and a significant association was observed under all five genetic models. Moreover, there was a significant association among Taiwanese under three genetic models, i.e., allele (C vs.
G: OR = 1.360, 95% CI 1.116-1.658, P = 0.002), heterozygote (CG vs. GG:
OR = 1.471, 95% CI 1.058-2.045, P = 0.022), and dominant (CC+CG vs. GG:
OR = 1.470, 95% CI 1.142-1.734, P = 0.001).
In the subgroup analysis regarding the source of controls, an increased risk of KD was found in the hospital-based subgroup under
all five genetic models, but not in the population-based subgroup. Furthermore, these data were further stratified by genotyping methods, a significant association between the ITPKC rs28493229 polymorphism and KD risk was observed in TaqMan and sequencing subgroup, in agreement with the overall data.
Between-Study Heterogeneity: There was no considerable heterogeneity detected between studies included in the analysis. The I2 value under two models, i.e., allele (C vs.
G: I2 = 51.87; PH = 0.042) and dominant (CC + CG vs. GG: I2 = 54.33; PH = 0.032) was greater than 50%, indicating that the included studies show heterogeneity.
Therefore, we have performed subgroup analyses by ethnicity, country, source of controls, and genotyping methods to explain the potential source of heterogeneity. As shown in
table 2, when subgroup analyses were performed, the between-study heterogeneity did not change considerably, indicating these factors might not be the major source of heterogeneity in this meta-analysis.
Sensitivity Analysis: Sensitivity analysis was performed to test the influence of individual studies on the stability of the overall ORs by omitting one study at a time.
The omission of any single study did not significantly affect the pooled ORs or 95%
CIs, suggesting the meta-analysis results may be reliable.
Publication Bias: Begg’s and Egger’s linear regression tests were used to assess the potential publication bias for the association between ITPKC rs28493229 polymorphism and KD risk in the overall meta-analysis. Table 2 lists the publication bias assessment method with its respective P-value for each test. The shapes of the funnel plots did not show any evidence of publication bias in the overall population, as shown in figure 4. Moreover, Egger’s test did not find any publication bias under all five genetic models, i.e., allele (C vs. G:
PBeggs = 0.386; PEggers = 0.216), homozygote (CC vs. GG: PBeggs = 0.763; PEggers = 0.408), heterozygote (CG vs. GG: PBeggs = 0.173;
PEggers = 0.225), dominant (CC + CG vs. GG:
PBeggs = 0.173; PEggers = 0.193), and recessive (CC vs. CG+GG: PBeggs = 0.548;PEggers = 0.523), suggesting no evidence of publication bias.
Discussion
To date, only a meta-analysis has been performed to evaluate the ITPKC rs28493229 polymorphism and KD risk in the global population in 2012.28 Therefore, it is necessary to perform an updated meta- analysis to evaluate the ITPKC rs28493229 polymorphism and KD risk based on previous and newly published eligible case-control studies. In the current study, we systematically reviewed and meta-analyzed the relationship between ITPKC rs28493229 polymorphism and KD risk. The results of this meta-analysis revealed a strong association between the ITPKC rs28493229 polymorphism and an increased risk of KD under all five genetic models.
Figure 4. The funnel plots of publication bias for association of the ITPKC rs28493229 polymorphism with risk of KD in the overall population. A: allele model (C vs. G); B: homozygote model (CC vs. GG); and
C: dominant model (CC+CG vs. GG)
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92 World J Peri & Neonatol 2022; Vol. 5; No. 2
Our pooled data strongly support the ITPKC rs28493229 polymorphism role in the development of pediatric KD. In 2008, Onouchi et al., found that the ITPKC rs28493229 polymorphism was significantly associated with increased susceptibility to KD and an increased risk of coronary artery lesions in US and Japanese children.14 Two years later, another case-control study performed by Chi et al. evaluated the association among 385 unrelated Taiwanese pediatric patients with KD (222 male and 163 female). The results of this study did not reveal a significant association between ITPKC rs28493229 polymorphism and KD risk KD or CALs in Taiwanese children.17 Khor et al., in a large case-control GWAS in 2,173 KD patients and 9,383 healthy controls evaluated the association of this locus with KD risk. Their results confirmed the previous findings of a genetic association in the region of the ITPKC gene.29 In 2018, Kim et al. in the most recently published study revealed a significant association between the ITPKC rs28493229 polymorphism and an increased risk of KD in Korean children.21 In 2012, Lou et al., in a meta-analysis based on seven case- control studies with 3,821 cases, and 12,802 controls evaluated the relationship of ITPKC rs28493229 polymorphism with KD risk under the allele genetic model. Their results showed a significant association between the C allele of rs28493229 polymorphism and an increased risk of KD (OR = 1.53, 95%
CI = 1.34-1.74, P < 0.001).28 However, their polled data remains an open field, because their data reliability and the number of included studies were smaller than that needed to receive a reliable conclusion.
Moreover, they evaluated the association only under the allele genetic model, and subgroup analyses were also not conducted.
Between-study heterogeneity is to be expected in a meta-analysis. The clinical variation, study design, ethnicity, sample size, source of controls, genotyping method, and HWE are among the major causes of heterogeneity.30-34 In the current meta-
analysis, we perceived that significant heterogeneity was found in two genetic models. After subgroup analyses by ethnicity, the heterogeneity still existed with a slight reduction. Meanwhile, other available variables including publication year, sample size, and genotyping method could not be considered as the source of the heterogeneity, suggesting the existence of other unknown factors influencing the heterogeneity among included studies.
Similar to other meta-analyses, there were several limitations in the current study. First of all, we only searched the literature issued in English and Chinese. Thus, potentially relevant papers published in other languages may not be identified, which might introduce potential selection bias. Second, the majority of the included studies in this meta-analysis were conducted on Asians which may introduce ethnicity bias, and further studies should perform on Caucasians and Africans.
Third, because of the limited study number in subgroup analysis, the power used to detect the association between ITPKC rs28493229 polymorphism and KD risk may not be strong enough. Fourth, there was a significant heterogeneity under the allele and dominant genetic models in the overall population, which could be because the analysis included few studies in the analysis or due to insufficient data that limited further subgroup analysis. So, more relevant case-control studies are required to be performed and then included in the meta-analysis to get more reliable and scientific data. Finally, this meta- analysis exclusively concentrated on the association between ITPKC rs28493229 polymorphism and KD risk without considering gene-gene or gene-environment interactions. Therefore, to comprehensively demonstrate the etiology of KD, it is extremely required to study the combined interaction of the related genes.
Conclusion
In summary, our pooled data revealed that the ITPKC rs28493229 was significantly
associated with susceptibility to childhood KD.
The results of this meta-analysis may improve our understanding of the role of ITPKC rs28493229 polymorphism in the etiology of KD, and aid in the diagnosis of high-risk patients with KD. Because of the limitations mentioned above in the meta-analysis, larger and well-designed studies in different ethnicities are needed to confirm our data.
Conflict of Interest
The authors have no conflict of interest.
Acknowledgments
We thank the anonymous reviewers for reviewing this manuscript.
How to Cite: Dastgheib SA, Asadian F, Tahooni A, Bahrami R, Noorishadkam M, Mirjalili SR, et al.
Genetic Association between ITPKC rs28493229 Polymorphism and Susceptibility to Kawasaki Disease: A Meta-Analysis. World J Peri &
Neonatol 2022; 5(2): 83-95.
DOI: 10.18502/wjpn.v5i2.11995
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